Esonye C, Onukwuli O D, Anadebe V C, Ezeugo J N O, Ogbodo N J
Department of Chemical Engineering, Alex Ekwueme Federal University, Ndufu-Alike, Abakaliki, Nigeria.
Department of Chemical Engineering, Nnamdi Azikiwe University, Awka, Nigeria.
Heliyon. 2021 Mar 8;7(3):e06342. doi: 10.1016/j.heliyon.2021.e06342. eCollection 2021 Mar.
This research presents optimal factor evaluation for maximum seed oil (DESO) extraction by applying central composite design (CCD) based on Box-Behnken (BB) experimental design of response surface methodology (RSM) and Artificial neural network (ANN) on feed forward-back propagation (FFBP) of Levenberg Marquardt (LM) training algorithm. Polar solvents (ethanol and combination of methanol and chloroform (M/C)) and non-polar solvents (n-hexane) were used for the extraction. The RSM optimal predicted oil yields were 45.21%, 38.61% and 30.87% while experimental values were 46.01%, 40.71% and 32.45% for n-hexane, ethanol and M/C respectively. The RSM optimum conditions were particle size of 450.67, 451.19 and 450.22μm, extraction time of 55.57, 55.16 and 56.11min and solute/solvent ratio of 0.19, 0.16 and 0.18 g/ml for n-hexane, ethanol and M/C respectively. The ANN-GA optimized conditions showed 5.14, 5.81 and 2.12 % higher DESO yields at 1.10, 0.26 and 0.65% smaller particle sizes, 5.47, 0.30 and 0.62 % faster extraction rate, and 24, 11.11 and 10% more solute requirement, for n-hexane, ethanol and M/C solvents respectively. The particle size was found to be the most significant factor. ANN and RSM established good correlations with the experimental data but ANN showed higher predictive supremacy than RSM based on its higher values of R and lower error indices. Also, ANN-GA provided more economical optimal DESO extraction route. The physico-chemical characteristics, functional groups and fatty acid compositions of the seed oil compared with literature values and suggest high commercial values for DESO. Therefore, the obtained results present a viable method to harness the useful and highly potential seed oil from for many industrial applications.
本研究基于响应面法（RSM）的Box-Behnken（BB）实验设计以及Levenberg Marquardt（LM）训练算法的前馈-反向传播（FFBP）人工神经网络（ANN），对最大种子油（DESO）提取进行了最佳因素评估。使用极性溶剂（乙醇以及甲醇和氯仿的混合物（M/C））和非极性溶剂（正己烷）进行提取。RSM预测的最佳出油率分别为正己烷45.21%、乙醇38.61%和M/C 30.87%，而实验值分别为正己烷46.01%、乙醇40.71%和M/C 32.45%。RSM的最佳条件分别为正己烷、乙醇和M/C的粒径为450.67、451.19和450.22μm，提取时间为55.57、55.16和56.11分钟，溶质/溶剂比为0.19、0.16和0.18 g/ml。ANN-GA优化条件表明，对于正己烷、乙醇和M/C溶剂，DESO产量分别提高了5.14%、5.81%和2.12%，粒径分别减小了1.10%、0.26%和0.65%，提取速率分别加快了5.47%、0.30%和0.62%，溶质需求量分别增加了24%、11.11%和10%。发现粒径是最显著的因素。ANN和RSM与实验数据建立了良好的相关性，但基于其较高的R值和较低的误差指数，ANN显示出比RSM更高的预测优势。此外，ANN-GA提供了更经济的最佳DESO提取路线。将种子油的物理化学特性、官能团和脂肪酸组成与文献值进行比较，表明DESO具有很高的商业价值。因此，所获得的结果提供了一种可行的方法，可从[具体来源未提及]中获取有用且极具潜力的种子油，用于许多工业应用。
